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Record W2936804990 · doi:10.5539/elt.v12n5p161

The Analytic Domain of Multiple- Intelligence and Its Relation to English Objective Test

2019· article· en· W2936804990 on OpenAlexvenueno aff
Baan Jafar Sadiq

Bibliographic record

VenueEnglish Language Teaching · 2019
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsTest (biology)PsychologyRelation (database)Domain (mathematical analysis)Mathematics educationSample (material)Computer scienceData miningMathematics

Abstract

fetched live from OpenAlex

The current research aims at identifying the analytic domain of multiple -intelligence and English objective test. The research is trying to answer that if there is a correlation between the analytic domain of multiple- intelligence and the English objective tests. Thus, the research has adopted a close questionnaire for diagnosing analytic domain (logical, rhythmic, and naturalistic) of multiple- intelligence of Iraqi students at Baghdad University, and an objective English test to achieve the aim of the research. Nine colleges at Baghdad University are randomly chosen to represent the sample of the research which is 511 students. The results of the research have shown that there are weak significant correlation between the analytic domain of multiple intelligent and the objective English test. Thus, from the results of the research Baghdad University could modify the objective tests with alternative ones that based on students’ ability and intelligence not guessing tests. Continuous long-term assessment, untimed, free- response format, individualized test and creative answers based on multiple- intelligence are recommended.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.303
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2019
Admission routes1
Has abstractyes

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